Examining protective effects of SARS-CoV-2 neutralizing antibodies after vaccination or monoclonal antibody administration

While new vaccines for SARS-CoV-2 are authorized based on neutralizing antibody (nAb) titer against emerging variants of concern, an analogous pathway does not exist for preventative monoclonal antibodies. In this work, nAb titers were assessed as correlates of protection against COVID-19 in the casirivimab + imdevimab monoclonal antibody (mAb) prevention trial (ClinicalTrials.gov #NCT4452318) and in the mRNA-1273 vaccine trial (ClinicalTrials.gov #NCT04470427). In the mAb trial, protective efficacy of 92% (95% confidence interval (CI): 84%, 98%) is associated with a nAb titer of 1000 IU50/ml, with lower efficacy at lower nAb titers. In the vaccine trial, protective efficacies of 93% [95% CI: 91%, 95%] and 97% (95% CI: 95%, 98%) are associated with nAb titers of 100 and 1000 IU50/ml, respectively. These data quantitate a nAb titer correlate of protection for mAbs benchmarked alongside vaccine induced nAb titers and support nAb titer as a surrogate endpoint for authorizing new mAbs.

The way of how errors in estimated parameters were estimated was not well described. Authors cite "bootstrap" -line supl 118 or "parametric bootstrap" line 203) but not very clear what "parameters" are bootstrapped and how. This must be better described. Providing codes for the analysis would be even better (along with the data!) I understand that IU/ml is the agreed upon units for Abs against COVID19. Some discussion is needed of how this is important, e.g., relate that to IC50 that is more typically calculated and used in analyses. How wrong can IC50 be for the analyses that are done here? (i.e., could you use IC50 and then Ab titer/IC50 to evaluate efficacy of two treatments)? Can you also translate IU50/ml to ug/ml for these specific Abs? (e.g., line 180-81).
To test equivalency of two protection parameters you could fit the data from all trials together assuming either one efficacy or different efficacies and compare using likelihood ratio test. This would be a more powerful approach than bootstrap. And it will give you true p value. Bootstrap cannot really give p values (line 249).
Authors state in discussion that having nAb titers for all individuals prior to exposure would be useful but not practical. Can they perform some power analyses to indicate how that could impact their conclusions?
Did your models fit the data well? Some evaluation of the quality of the model fit to data (and alternative models tested) must be presented.

Minor concerns
It is unfortunate that the data from 2 published trials and from COVE trial are not publicly available. This is unacceptable. The prescribed recipe by authors to share the data is also unacceptable. I could think of 2-4 groups that could do the same analysis if they had access to the data, and with different teams approaching the same PUBLISHED data we would have more solid results at the end, benefitting all humanity! When mentioning data from COVE there should be a citation of where the data came from. If this is a new study, this must be stated.
In papers involving modeling, equations must be numbered to authors and reviewers can refer to them.
I think some of the mathematical details -e.g., models for nAb dynamics and protection should be in the main text, not supplement. As this forms the basis of the paper, readers should have it upfront without looking for supplement.
I understand the potential issues with patient privacy but given that trials were done prior to Delta, this is probably already not an issue.
Sup. Line 114-115. This fit by Poisson model should be explicitly described and shown.
Why does the first author have Phd to the name and others don't. Is that because others don't have degrees? Seems like a bias here.
Line 166 -95 pound individual. Is that a child? I can hardly imagine this for an adult. Please discuss in more detail.
Decay rate of Abs cannot be log10 IU50/ml per day. Decay rates are in per day units. The proper way to model these is not with log10 but with natural log. Please consult the expert on modeling Ab dynamics.
Line 282-283 -do we really know that nAbs are the major protector against COVID19? Please give the citation that accurately quantifies this! 285-286 -the fact that nAbs correlate with protection does not mean much. What does not correlate with protection?      Remarks to the Author: Thank you for inviting me to review this manuscript. The authors evaluted NAb correlates of protection in COV-2069 (Casivirimab+Indevimab prophylaxis) trial and the mRNA-1273 vaccine COVE trial. The results have significant practical implications for how new monoclonal antibody authorisation could be viewed moving forwards, given the discrepancy between use of neutralisation surrogates for emergency authorisation of booster vaccines doses, whereas new MAbs require clinical efficacy endpoints are present. A particular strength is that the authors standardise their output to WHO IU50/mL, allowing comparison with other correlate of protection outputs.
For the MAb that was assessed, the authors conclude that predicted NAb titres at the time of exposure of 1000 IU50/mL was associated with protective efficacy of 92% (84-98%), with a PE of >80% achieved with a NAb titre of 398 IU50/mL (25-631), and a titre of 200 IU50mL (5-501) associated with a 50% PE.
For mRNA-1273, a titre at the time of exposure to 100IU50/mL was associated with a VE of 93% (91-95%) and a titre of 1000 IU50/mL associated with 97% (95-98%) VE. Figure 4 demonstrating the relationship between VE and PE at given NAb titres for vaccines and MAb respectively is especially interesting and important, and a particular strength of the approach to compare the two.
A major limitation is of course that in effect only efficacy against pre-delta/pre-omicron SARS-CoV-2 could be assessed, which the authors acknowledge.
I have minor comments and suggestions: 1. For the COV-2069 study, the authors state that the study period was Jul 2020 -Oct 2021, yet no delta infections were included. This is unusual given the duration when delta circulated globally, but I assume it is because the recruitment period was early in this window. Please clarify this in the text.
3. The issue of whether vaccine induced antibodies may have confounded the MAb concentration vs PE in COV-2069 needs some further clarification and discussion. Looking at Herman et al, vaccines were prohibited prior to randomisation but allowed during follow up (with advise to follow CDC recommendations to receive these >90 days following MAb). 34.8% received at least 1 dose during the follow up period, with a median time to first vaccine 108.5 and 109 days in the two groups. It looks like only 3 participants developed symptomatic infection AFTER vaccination (1 in MAb and 2 in placebo), which suggests that vaccine effects should not have had much effect. This is worth stating up front.
4. The authors mention the total group sizes for the COVE trial. If I am not mistaken, looking at Gilbert et al, the serum substudy included antibody data from 36 post-day 57 cases and 1005 noncases. This should be stated if the data derived for analysis of predicted NAb at time of exposure was from this sub-cohort. 5. Please include the median FU duration of the studies in the text (I appreciate it is shown in the figure) Reviewer #3: Remarks to the Author: In this manuscript, Follmann and colleagues correlate neutralizing serum titers after monoclonal antibody administration with protection from symptomatic COVID-19 observed in a clinical trial. Determining a quantifiable correlate of protection (i.e., neutralization titers) for monoclonal antibodies may aid the development of antibody-based strategies for COVID-19 prevention. They compare titers and protection after antibody infusion with those determined in a mRNA-1273 vaccine trial. Based on similar protection at lower neutralization titers in the vaccine trial, they conclude that vaccines provide protection that goes beyond antibody-mediated neutralization.
Serum neutralization for the analyses was not specifically measured longitudinally but modeled/predicted. For the monoclonal antibodies, this was based on previously determined pharmacokinetic data of antibody concentrations and a correlation of some datapoints with experimentally determined neutralization titers. For the vaccine study, neutralization titers were predicted based on a decay factor derived from longitudinal neutralization data previously determined in vaccinees using a comparable pseudovirus assay Strengths of the manuscript include -Both the monoclonal antibody trial and the vaccine trial were conducted prior to the emergence of the Delta and Omicron variants of SARS-CoV-2 (and not in an environment with high rates of Beta circulation). Therefore, the results of both trials are broadly comparable from the perspective of neutralizing antibody sensitivity of the exposing viral strains.
-Neutralization titers are provided as WHO International Standard Units against (ancestral) D614G pseudovirus and the (limited number of) experimentally obtained neutralization titers was determined using comparable pseudovirus assays.
-Many limitations are appropriately acknowledged in the manuscript.

Main limitations and suggestions:
-The very wide confidence intervals due to the small number of cases prevent drawing firm conclusions for neutralization titers correlating with monoclonal antibody-mediated protection below approximately 300 IU50/ml. In addition, low neutralization titers for the vaccine study could not be assessed because of the limited follow-up period. The actual data on which the lower half of Figure 6 ("low titer") is based, is therefore quite limited.
-In the Supplementary Materials on the "Deconstruction analysis" to quantify extant nAb effects, the authors describe that they separate (1) circulating and mucosal antibodies and (2) all other aspects of vaccine-induced immunity. This is also partially reflected in the legend to the scheme shown in Figure  6 ("role of extant circulating and mucosal antibody in vaccine induced protection"). All analyses are, however, based on the assessment of neutralizing serum titers as no mucosal samples were analyzed. Whether mucosal neutralizing activity is comparable for recipients of monoclonal antibodies and vaccinees is not shown.
-In their description of nAb titers ( Figure 5), the authors state that "it is clear that an anamnestic response tends to be invoked in those with lower titers at symptom onset". From at looking at the Figure, it is not necessarily that "clear" that individuals with lower titers show an increase in neutralization after breakthrough activity but individuals with higher titers do not. It would be helpful to provide a stratified analysis with individuals grouped into "high" and "low" neutralization titers. Whether the stated observations hold true for individuals with asymptomatic infection remains speculative (all studied individuals had symptomatic infections).
-All analyses and results apply to the pre-Omicron era (as briefly stated in the section on study limitations). Neutralization titers against Omicron after two mRNA vaccine doses are typically very low. After post-vaccination breakthrough infection, a strong increase in neutralizing serum titers against Omicron has repeatedly been demonstrated (recall response, including for breakthrough infections with a non-Omicron variant). Nevertheless, vaccine effectiveness against Omicron-mediated symptomatic COVID-19 after two mRNA vaccine doses is very low. Thus, symptomatic infection can frequently occur in vaccinated individuals with low neutralization titers despite a nAb recall response. This aspect should be highlighted more. This is especially true as non-nAb-based mechanisms of protection are likely not as affected by Omicron mutations, even more suggesting a role for high antibody titers in preventing symptomatic COVID-19.
-In the section on study limitations, the authors note that testing asymptomatic infection in the COV-We respond to each reviewer comment in italics below. Line references included match the clean version of the revised manuscript.

REVIEWER COMMENTS
Reviewer #1 (Remarks to the Author):

Positive feedback
It is very nice that the authors had access to very clean data from household exposure and protection by nAb trial. The analysis seems reasonable for most parts (see comments), and most details of the data and analyses are relatively well described. The analysis of comparing protection efficacy by nAbs vs. mRNA vaccine is reasonable (although there are alternative ways of doing it). Overall, this is a statistically solid paper with good analyses.

Thank you!
Major concerns 1) Several mathematical details of the analysis require improvement to judge if the analysis is truly valid. For example, the model for predicted nAb titer is not described by equations (in lines [184][185][186][187][188][189][190], only by words. The details from supplement must be put here. Why was this approach chosen for analysis? Why not mixed effect modeling that is more common to describe PKPD of drugs for diverse groups of people? This must be made better. Also, I do not see any alternative analyses/assumptions for the model. E.g., why was the specific choice for models (suppl. lines 67-70)? Authors should explore whether any alternative ways of choosing the parameter distribution results in different answers.
The predicted nAb titer equation (lines 184-190) was based on 18 samples selected from COV-2069 samples that covered the range of follow-up and that had both concentration and neutralization titer measured. These data points are displayed in Figure 2B and show a clear linear relationship between log concentration and log ID50. We thus estimated a linear requestion using least squares regression obtaining Y = 1.80 + 1.15 X (Figure 2 Panel B), where X is the log10 concentration of CAS+IMB and Y the log10 ID50 neutralization titer. Given the striking linear relationship we did not consider other models. The rewritten section (lines 1626-176) is given below which also now describes the population PK modeling used to generate the concentration curves ``For COV-2069 trial participants, pharmacokinetic (PK) antibody concentration curves of casirivimab and imdevimab in serum over 8-months were estimated using population PK models developed for casirivimab and imdevimab from three clinical studies. The population PK models were two compartment models with linear elimination and first-order absorption following subcutaneous dosing (25) Concentrations of casrivimab and imdevimab at each time point were added to obtain concentrations of casirivimab and imdevimab combined in serum. Stochastic simulations with interindividual random effects were used to predict casirivimab and imdevimab concentration by weight and sex for each day of follow-up. Figure 2A displays the concentration time profiles for 10 randomly selected individuals. To transform antibody concentration to pseudo-virus neutralization titer, we conducted a separate experiment where 27 frozen serum samples from COV-2069 participants that spanned the course of follow-up were selected. Eighteen samples had both concentration measured and neutralization titer above the limit of detection. We estimated the relationship between the concentration of antibodies and the neutralization titer for these 18 samples as log10(ID50) = 1.80 + 1.15 x log10 (concentration), Figure 2B. We used this equation to determine individualized neutralization titer decay curves ( Figure 2C). '' Regarding the linear model on lines 67-70 we also fit a model that allowed a bend-point to accommodate biphasic decay but this had virtually the same fit as the simpler model with linear decay of log10 titer. The Wald statistics p-value for testing a bend-point was 0.54.
Following up on the math. Is the approach to model censored data appropriate? This is a big field in statistics and some arguments are needed to justify better the chosen approach with citing appropriate literature. E.g., one example of how censored data could be used in likelihood methods (PMID: 17489970).

Censoring could apply both to the antibody data (censored at the limit of detection) or the time to COVID-19 (censored by the end of follow-up).
We use standard Cox regression models for the censored time to event data. Such models were used for the COVE analysis (Gilbert, Peter B., et al.

"Immune correlates analysis of the mRNA-1273 COVID-19 vaccine efficacy clinical trial." Science 375.6576 (2022): 43-50) and many other COVID-19 vaccines trials. See supplemental materials, Vaccine induced antibody risk model section.
In the supplemental materials "Modelling antibody kinetics of mRNA-1273" section, three datapoints were below the limit of detection. We used a likelihood-based analysis and the model assumes that antibody decline continues log-linearly below the limit of detection and censoring is a feature of the assay, but not the underlying biological process. The model integrates over the possible values below the limit of detection.
The datasets utilized for the development of population PK models for casirivimab and imdevimab were comprised of approximately 11,000 samples each. For casirivimab and imdevimab, <1.78% and <1.56% of these samples were below the limit of quantitation (BLQ), and as such, the BLQ samples were excluded from analysis.
3) Why models (line 102, 108) are good models? What would be alternatives? One question I have is how to relate Ab titer to protection. Authors for some reason chose log10 Ab as protection predictor. That seems to be inconsistent with physics/biology were protection should be linearly-dependent on Ab levels. Other models are also possible (e.g., see PMID: 32453765 for examples of alternative models a rigorous modeling study tries). Please investigate.
Thank you for the opportunity to clarify. Indeed, model evaluation was undertaken. We used likelihood ratio tests to consider different models for COV-2069 as given below.
where d is days since infusion and Ab (d) We chose the 3PL based on these tests and on biological plausibility---PE=0 with no antibodies, but it also asymptotes to 1 − as Ab(d) goes to infinity. For completeness, we also report the log-linear model (B) in the supplementary materials in Figure S6 to contrast with the analogous vaccine VE curve. Use of the log-linear model results in somewhat stronger conclusions compared to the 3PL.
We added the following text to the paper (lines 211-212) and cite the Gilbert study referenced above: "Log linear VE curves were used in a prior analysis of COVE and multiple vaccine trials thus allowing direct comparisons with other studies.(34)" Your second question concerns choice of log10 Ab. This is the standard readout for neutralization antibodies in the USG sponsored COVID-19 vaccine studies, so its use here facilitates comparison. For thoroughness we tried to fit model (B) using ID50 titer as the covariate instead of Ab=log10(ID50), but this model did not converge.
Note that for these log-linear hazard models, risk is proportional to exp() of log10-antibody ID50 titer. Thus, in model B, risk is proportional to a power of ID50 titer. To see this, assume for simplicity that time to infection follows an exponential distribution. Define Ab=log10(x) where x is the predicted ID50 neutralization titer. Thus, the hazard for COVID-19 for a mAb participant is Note is a parameter that can assume any value on the real line and thus can equal 1/ ( ) in which case risk is directly proportional to titer. Other values of , are possible and allow for a richer class of models.
4) The way of how errors in estimated parameters were estimated was not well described. Authors cite "bootstrap" -line supl 118 or "parametric bootstrap" line 203) but not very clear what "parameters" are bootstrapped and how. This must be better described. Providing codes for the analysis would be even better (along with the data!) Thanks for bringing this up, indeed it was an involved process and not clearly described. A new description is given in the supplementary materials Monoclonal antibody risk model section, which we provide below.
We obtain 95% pointwise confidence intervals for the parameters in (S2) <i.e. model (D) above> via the bootstrap and propagate uncertainty about the relationship between concentration and titer by a random draw from the bivariate distribution of the slope and intercept as estimated from the 18 paired samples. Details follow. We first estimated the parameters from the below equation  Figure S6 rejects at p<0.001." 7) Authors state in discussion that having nAb titers for all individuals prior to exposure would be useful but not practical. Can they perform some power analyses to indicate how that could impact their conclusions?
Use of actual titer would presumably sharpen our results because actual titer would be a more precise measure of the biology on any given day. Necessity required this simpler approach. (2), 403-409.) While these calculations are more difficult for COV-2069 we anticipate var(x)/{var(x) + var(e) } would be even larger due to the greater variability in titer over follow-up for the mAb (see Figure 3). 8) Did your models fit the data well? Some evaluation of the quality of the model fit to data (and alternative models tested) must be presented.

To get a handle on the effect of using predicted titer instead of actual titer, one can view our approach as a regression calibration method to the problem of a covariate measured with error. For simplicity consider a linear regression model E(Y)= B0 + B1 x where x is the true/actual value of the covariate, but we use a covariate which is the expected value of x based on a model (i.e. that day's predicted assay readout). Regression calibration results in an attenuated estimate of B1. The degree of attenuation depends on the variation in x in the study compared to the variance of the prediction error ( -x). For COVE, we conservatively approximate var(x) by the sample var( ) throughout follow-up and approximate the variance of prediction error ( -x) from the error variance estimate from the Doria-Rose data. These estimates are var( ) = 1.10 and var( -x) = 0.02 and thus var(x)/(var(x) + var(e) ) is approximated by 0.98. This is a setting where regression calibration should work quite well with little attenuation. (see e.g., Follmann, D. A., Hunsberger, S. A., & Albert, P. S. (1999). Repeated probit regression when covariates are measured with error. Biometrics, 55
To assess goodness of fit we compared the Kaplan-Meier curves of cumulative incidence with the modelbased cumulative incidence, see below for the COVE and COV-2069 studies, respectively. A feature of these data is that the placebo arm is fit quite well because the vast majority of cases are from the placebo arms in both trials. We see that the model based cumulative incidence curve for the vaccine arm lies within the confidence band for the Kaplan-Meier curve and nearly so for the mAb arm which is more variable due to having few events in that arm.

We include the above information in the supplemental materials, section Model Goodness-of-Fit
In prior comments we evaluated alternative models for COV-2069. Below we fit some alternative models for COVE along with the log-likelihoods for each.

Model
Model

Minor concerns
1) It is unfortunate that the data from 2 published trials and from COVE trial are not publicly available. This is unacceptable. The prescribed recipe by authors to share the data is also unacceptable. I could think of 2-4 groups that could do the same analysis if they had access to the data, and with different teams approaching the same PUBLISHED data we would have more solid results at the end, benefitting all humanity!
We understand this concern and added the following specific data availability statements to the paper based on the study sponsors. 2) When mentioning data from COVE there should be a citation of where the data came from. If this is a new study, this must be stated.

In lines 138-138 we clarify that the analysis of Gilbert et al., is through the end of the blinded phase and state we use this dataset on line 142. More details about the dataset are included in the supplemental materials Vaccine induced antibody risk model section.
We also use data from Follmann, Janes et al., and state this on lines 149 of the paper.
3) In papers involving modeling, equations must be numbered to authors and reviewers can refer to them.

We now include equation numbers in both the paper and the supplemental materials.
4) I think some of the mathematical details -e.g., models for nAb dynamics and protection should be in the main text, not supplement. As this forms the basis of the paper, readers should have it upfront without looking for supplement.

We now provide the equation linking the mAb concentration to neutralization titer as well as the PE and VE hazard models, with equations numbers, in the main text and state on lines 209-218:
"For COVE, a log-linear curve was used where Ab is log10(ID50) and expit(a)=exp(a) /(1+exp(a)). This curve allows for flexible modeling of a wide range of PEs. The maximal protective efficacy is given by (1 − ), the ratio − / determines the level of Ab where the maximal protective efficacy of (1 − ) is halved, and as ID50 goes to 0, PE goes to zero. We also estimated a log-linear curve for PE as a sensitivity analysis." 5) I understand the potential issues with patient privacy but given that trials were done prior to Delta, this is probably already not an issue.
See above for the company policies regarding data sharing.
6) Sup. Line 114-115. This fit by Poisson model should be explicitly described and shown.
We describe the model and report bootstrap confidence intervals for the 50% PE and saturation parameters in lines 257-258. The 50% PE is similar to but more meaningful than the halving parameter. We added the following to the supplemental materials, Monoclonal antibody risk model section: "We first estimated the parameters from the below equation We now introduce the details of the weighted VE estimation in the supplemental materials Vaccine induced antibody risk model section:

``Instead of assessing Day 57 antibody in all 14,202 vaccine arm participants Gilbert et al., (8) used a case-cohort design which was comprised of a stratified random sample of 1010 participants who comprised the immunogenicity sub-cohort, plus 36 disease cases (5 of which were in the set of 1010).''
More details are given after that, but in essence, each sampled person is weighted so that the weighted distribution of patient characteristics matches the distribution of characteristics of the 14,202. 8) Why does the first author have Phd to the name and others don't. Is that because others don't have degrees? Seems like a bias here.
Thanks for catching this, this was an inadvertent error on our part and has been corrected. 9) Line 166 -95 pound individual. Is that a child? I can hardly imagine this for an adult. Please discuss in more detail.
We looked into this and found that it was an adult woman with small stature. Note that for a 4' 10" women a healthy weight ranges form 91 to 115 pounds. See https://www.nhlbi.nih.gov/sites/default/files/media/docs/Session6-BMIChart-508.pdf. We deleted this text to avoid confusion.
10) Decay rate of Abs cannot be log10 IU50/ml per day. Decay rates are in per day units. The proper way to model these is not with log10 but with natural log. Please consult the expert on modeling Ab dynamics.
Thank you for your comment. We converted change per day in log10 units to natural logs to determine the half-life. Specifically, for the standard exponential decay formula ( ) = (0) (− ), we determined = ln (10) where B was the change per day in log10 units. This allowed us to determine the half-life for monoclonal Ab decay as log(2)/(log(10)*0.012) = 26 days and for vaccine Ab decay as log(2)/(log(10)*0.0043) = 70 days. We've rewritten this section so that we only report the half-lives in the results Neutralization Titer Kinetics section. We provide the above formula in the supplementary materials.
"For participants in the monoclonal antibody COV-2069 trial, the median half-life of the neutralization titer was 26 days. For vaccinated individuals in the COVE trial, the estimated half-life was 70 days." 11) Line 282-283 -do we really know that nAbs are the major protector against COVID19? Please give the citation that accurately quantifies this! Thank you for pointing this out. There is not a citation that we know of that qualifies this statement, therefore it was deleted.
12) 285-286 -the fact that nAbs correlate with protection does not mean much. What does not correlate with protection?
The correlation of nAbs with protection was a central goal of the USG supported Operation Warp Speed vaccine trials. The establishment of this correlation has allowed the use of neutralization titer to predict efficacy for variants based on titers against variant pseudo-virus neutralization assays and has allowed licensure of variant vaccines by regulatory agencies without running large Phase III clinical trials (see e.g., Gilbert, Peter B., et al. "A Covid-19 Milestone Attained-A Correlate of Protection for Vaccines." New England Journal of Medicine 387.24 (2022): 2203-2206 further data is needed to definitively quantitate the contribution. Studies to quantify the role of T cells and other mechanisms vis-à-vis antibodies are ongoing and of great interest. Figure 1 -typically it is better to have placebo and then treatment groups discussed.

13)
Thank you, the order has been reversed in figure 1. 14) Figure 2 -I could not read any axes, I could not understand different lines (B&W printer). This must be improved, and add legends to plants to explain things -Add A/B/C to different panels. This is meant to evoke that there is more uncertainty for titers below 10^2.5. We now explain this in the caption. Figure 5: you may want to add information on the kinetics of Ab decay -e.g., half-life per patient or by fitting decay models with mixed effect models.

17)
Thank you, we now report in our results section, lines 285-287: ``Based on random effects modeling, the half-lives of antibody abundance from 28 days post 2 nd dose to the onset of symptoms was estimated as 58 days for binding antibody and 62 days for neutralization antibodies." 18) Figure 6: add quantitative numbers to what low titer and high titer are in the figure.
Low and high are not precise numbers but cover a range. We convey the idea of a range in the caption and describe higher as e.g., 1000 IU50/ml and lower as e.g., < 100 IU50/ml. This is meant to show the data. We report the parameter estimates of the slope of decay, , and its confidence interval in the text of the supplementary materials. 20) Fig S2: using least squares is inappropriate here because error is in both x/y axes. Use Deming regression or similar.

19) Figures
Thank you, we now using Deming regression. We updated this in figure S2 and in the text of the paper (line 206).

21) Figure S3: show numbers of patients and tests statistics as done in proper figures on clinical trials.
We now provide the number at risk and report the log-rank p-value. Figure S4: I cannot read axes. Can you also show the data fitted? Same for Fig S5 The labels and axes font sizes have been increased. The x-axis shows the predicted log10 neutralization at disease onset for both the mAb and placebo arms and this has also been increased in size.

22)
Reviewer #2 (Remarks to the Author): Thank you for inviting me to review this manuscript. The authors evaluted NAb correlates of protection in COV-2069 (Casivirimab+Indevimab prophylaxis) trial and the mRNA-1273 vaccine COVE trial. The results have significant practical implications for how new monoclonal antibody authorisation could be viewed moving forwards, given the discrepancy between use of neutralisation surrogates for emergency authorisation of booster vaccines doses, whereas new MAbs require clinical efficacy endpoints are present. A particular strength is that the authors standardise their output to WHO IU50/mL, allowing comparison with other correlate of protection outputs.

Thank you!
For the MAb that was assessed, the authors conclude that predicted NAb titres at the time of exposure of 1000 IU50/mL was associated with protective efficacy of 92% (84-98%), with a PE of >80% achieved with a NAb titre of 398 IU50/mL (25-631), and a titre of 200 IU50mL (5-501) associated with a 50% PE.
For mRNA-1273, a titre at the time of exposure to 100IU50/mL was associated with a VE of 93% (91-95%) and a titre of 1000 IU50/mL associated with 97% (95-98%) VE. Figure 4 demonstrating the relationship between VE and PE at given NAb titres for vaccines and MAb respectively is especially interesting and important, and a particular strength of the approach to compare the two.
A major limitation is of course that in effect only efficacy against pre-delta/pre-omicron SARS-CoV-2 could be assessed, which the authors acknowledge.
I have minor comments and suggestions: 1. For the COV-2069 study, the authors state that the study period was Jul 2020 -Oct 2021, yet no delta infections were included. This is unusual given the duration when delta circulated globally, but I assume it is because the recruitment period was early in this window. Please clarify this in the text.
Thank you. We made an error in this statement and have changed it to read (lines 110-111): "The study was conducted prior to the emergence of Omicron-lineage VOC." 2. It isn't fully clear how the IU50/mL equivalents of MAb concentration were generated and in which samples (what source/how chosen). Please expand on this in the methods.
Twenty-seven samples were selected from COV-2069 to cover the course of follow-up. Criteria included not being vaccinated or seropositive, no evidence of prior RT-qPCR positive test, with a pristine sample and available backup samples. 18 of the samples had neutralization titers above the limit of detection with concentration data and were used to estimate the linear relationship between concentration and neutralization titer. We now write in the Neutralizing Antibody Data section (lines 170-178): ``To transform antibody concentration to pseudo-virus neutralization titer, we conducted a separate experiment where 27 frozen serum samples from COV-2069 participants that spanned the course of follow-up were selected. Eighteen samples had concentration measured and neutralization titer above the limit of detection. We estimated the relationship between the concentration of antibodies and the neutralization titer for these 18 samples was estimated as log10(ID50) = 1.80 + 1.15 x log10(concentration), Figure 2B.'' 3. The issue of whether vaccine induced antibodies may have confounded the MAb concentration vs PE in COV-2069 needs some further clarification and discussion. Looking at Herman et al, vaccines were prohibited prior to randomisation but allowed during follow up (with advise to follow CDC recommendations to receive these >90 days following MAb). 34.8% received at least 1 dose during the follow up period, with a median time to first vaccine 108.5 and 109 days in the two groups. It looks like only 3 participants developed symptomatic infection AFTER vaccination (1 in MAb and 2 in placebo), which suggests that vaccine effects should not have had much effect. This is worth stating up front.
Thank you for this important point, we now state in the methods section (lines 117-120): "While vaccines were prohibited prior to randomization, 35% of participants reported receiving at least one dose during the follow-up period with a median of 109 days to first dose and were included in our modified intent-totreat (mITT)analysis set. Three participants (two on placebo) developed symptomatic infection after vaccination." We also mention this 'lack of compliance' as a limitation in the discussion which might weaken estimated relationships. We state on lines 371-372, "In the COV-2069 trial some vaccination occurred which might weaken our results." 4. The authors mention the total group sizes for the COVE trial. If I am not mistaken, looking at Gilbert et al, the serum substudy included antibody data from 36 post-day 57 cases and 1005 non-cases. This should be stated if the data derived for analysis of predicted NAb at time of exposure was from this subcohort.
In lines 142-144we state: Here, we use the same data as Gilbert et al.,(8) but correlate the incidence of COVID-19 events with predicted neutralization titers throughout follow-up, rather than at day 57, thus analyzing titer as an exposure proximal correlate.
We now introduce the details of the VE estimate with the following in the Supplementary Materials. "Instead of assessing Day 57 antibody in all 14,202 vaccine arm participants Gilbert et al.,(8) used a case-cohort design which was comprised of a stratified random sample of 1010 participants who comprised the immunogenicity sub-cohort, plus 36 disease cases (5 of which were in the set of 1010)."

Please include the median FU duration of the studies in the text (I appreciate it is shown in the figure)
We now include, for COVE, "and the median follow-up was 157 days" on line 136. We now state for COV-2069 on line 114, ``The median follow-up was 225 days." Reviewer #3 (Remarks to the Author): In this manuscript, Follmann and colleagues correlate neutralizing serum titers after monoclonal antibody administration with protection from symptomatic COVID-19 observed in a clinical trial. Determining a quantifiable correlate of protection (i.e., neutralization titers) for monoclonal antibodies may aid the development of antibody-based strategies for COVID-19 prevention. They compare titers and protection after antibody infusion with those determined in a mRNA-1273 vaccine trial. Based on similar protection at lower neutralization titers in the vaccine trial, they conclude that vaccines provide protection that goes beyond antibody-mediated neutralization.
Serum neutralization for the analyses was not specifically measured longitudinally but modeled/predicted. For the monoclonal antibodies, this was based on previously determined pharmacokinetic data of antibody concentrations and a correlation of some datapoints with experimentally determined neutralization titers. For the vaccine study, neutralization titers were predicted based on a decay factor derived from longitudinal neutralization data previously determined in vaccinees using a comparable pseudovirus assay

Strengths of the manuscript include
Both the monoclonal antibody trial and the vaccine trial were conducted prior to the emergence of the Delta and Omicron variants of SARS-CoV-2 (and not in an environment with high rates of Beta circulation). Therefore, the results of both trials are broadly comparable from the perspective of neutralizing antibody sensitivity of the exposing viral strains.
Neutralization titers are provided as WHO International Standard Units against (ancestral) D614G pseudovirus and the (limited number of) experimentally obtained neutralization titers was determined using comparable pseudovirus assays.
Many limitations are appropriately acknowledged in the manuscript.

Thank you!
Main limitations and suggestions: 1) The very wide confidence intervals due to the small number of cases prevent drawing firm conclusions for neutralization titers correlating with monoclonal antibody-mediated protection below approximately 300 IU50/ml. In addition, low neutralization titers for the vaccine study could not be assessed because of the limited follow-up period. The actual data on which the lower half of Figure 6 ("low titer") is based, is therefore quite limited.